Large universities today operate technology ecosystems that rival those of global enterprises. They manage thousands of users, dozens of internally built and third-party applications, and complex cloud environments that support teaching, research, and administration.
For one of the top universities in the United States, application security became increasingly difficult to manage as development velocity increased. Teams were shipping faster, infrastructure was becoming more distributed, and the attack surface was expanding—while security resources remained limited.
The university needed a way to reduce real security exposure without slowing development or overwhelming teams with noise.
The environment
The university’s application landscape was highly decentralized by design:
- Multiple development teams operating across academic and administrative departments
- CI/CD pipelines supporting frequent releases and continuous updates
- Public-facing applications accessed by students, faculty, and external users
- Internal systems handling sensitive academic, financial, and research data
- A mix of modern cloud-native services and long-running legacy applications
Security governance was centralized, but execution was not. Any security approach that relied on manual reviews or rigid controls simply did not scale.
Where traditional security fell short
Despite having standard security tools in place, meaningful risk reduction was hard to achieve.
Too much data, not enough insight
Security scans generated large volumes of findings, but most lacked context. Teams could see what existed, but not what mattered. Vulnerabilities were treated as equal regardless of reachability or exploitability.
Persistent false positives
Many reported issues lived in unreachable code paths or configurations that were not exploitable in production. Over time, developers began to distrust security alerts, slowing response times.
Prioritization based on theory, not reality
Without clear visibility into how vulnerabilities could actually be exploited, prioritization relied heavily on severity scores and manual judgment. High-effort remediation work often delivered minimal risk reduction.
Growing friction between teams
Security reviews increasingly felt disconnected from real development workflows. Developers wanted clarity and speed; security teams needed confidence and control. The gap between the two continued to widen.
Rethinking application security
Rather than adding another point solution, the university focused on changing how application risk was understood and managed.
The security team aligned around a simple principle:
risk should be measured by exploitability, not volume.
To support this shift, they looked for a platform that could:
- Identify vulnerabilities that were actually reachable in real execution paths
- Prioritize issues based on real-world attack potential
- Provide developers with clear, actionable remediation guidance
- Integrate directly into existing CI/CD pipelines
This led to the adoption of CloudDefense.AI as part of the university’s application security program.
Deployment without disruption
The rollout focused on minimizing friction and maximizing early value.
CloudDefense.AI was integrated into existing CI/CD workflows, allowing security analysis to run alongside normal development activity. There was no need for manual gates or new approval processes. Instead, security findings became part of the same feedback loop developers already trusted.
The security team used the platform to standardize how risks were evaluated across applications, while development teams received consistent, developer-friendly outputs regardless of department or project.
What changed in practice
Once CloudDefense.AI was in place, the way teams approached security changed fundamentally.
Reachability became the baseline
Vulnerabilities were no longer treated as abstract findings. Teams could see which issues were actually reachable through real execution paths and which ones posed no practical risk.
Noise dropped dramatically
By filtering out non-actionable findings, the overall volume of alerts decreased sharply. Developers no longer had to sift through large lists of low-impact issues to find what mattered.
Faster and more confident remediation
Security conversations became more concrete. Developers understood why an issue mattered, where it existed, and how to fix it. This reduced back-and-forth and shortened remediation cycles.
Better alignment between security and engineering
Because prioritization was grounded in real exposure, discussions shifted from opinions to evidence. Security became an enabler rather than a blocker.
The outcome
After adopting CloudDefense.AI, the university observed clear and measurable improvements:
- 98% reduction in application security risk
- Significant decrease in false positives and alert fatigue
- Faster remediation across multiple development teams
- Improved confidence during internal assessments and audits
- A scalable AppSec workflow without increasing security headcount
Most importantly, security teams could focus on preventing real attacks instead of managing endless alerts.
Why this approach works
Modern application environments demand security that understands context. Simply finding vulnerabilities is no longer enough. What matters is knowing which ones are exploitable, which ones matter now, and which ones can safely be deprioritized.
This approach allowed the university to strengthen its security posture while preserving developer velocity—a balance that many large organizations struggle to achieve.
Conclusion
By adopting CloudDefense.AI, this top U.S. university transformed its application security program from alert-driven to outcome-driven.
The result was not just improved visibility, but measurable risk reduction, improved developer trust, and a scalable security model aligned with modern software development.
For organizations navigating similar complexity, this case highlights what’s possible when security is built around context, exploitability, and real-world risk—not just numbers on a dashboard.
